International Journal of Social Computing and Cyber-Physical Systems
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International Journal of Social Computing and Cyber-Physical Systems (6 papers in press)
Deep learning-based medical expert system for diabetes diagnosis on IoT healthcare data by K. Vijayaprabakaran, K. Sathiyamurthy, S. Sowmya Abstract: Machine learning in internet of things (IoT) plays a vital role in diagnosing diseases and predicting the risk level of health by analysing patient health records. Diabetes has been commonly seen in all age groups of peoples. Early diagnosis of diabetes and proper medication will help the patient live normally for long life. In this study, various machine learning techniques were experimented to diagnose diabetes and the results were compared. In order to diagnose diabetes from the health record of the patient, this work proposes a deep learning-based expert system (DL-Expert sys). The DL-Expert system predicts the risk level of the patient and provides the recommendation of the diet to the patient. The experimental results illustrate that the predictive model using deep learning algorithm of RNN with LSTM achieves higher accuracy than logistic regression, Naive Bayes and neural network. Keywords: machine learning simple linear regression; multiple linear regression; logistic regression; Naïve Bayes; recurrent neural network with GRU; recurrent neural network with LSTM.
Secure resource sharing in grid system without public key settings by Manik Lal Das Abstract: Grid system involves the collaborative use of computers, networks, devices, software, databases and interfaces maintained by multiple organisations. Grid security has attracted increasing interests from researchers, as multiple entities in grid system require to deliver different nature of services. Grid security is primarily guided by the grid security infrastructure (GSI) that uses public key cryptography (PKC) for authentication, delegation and resource sharing between the communicating entities. Although GSI provides required security properties to grid system, PKC-based security solution is computationally expensive in comparison to symmetric key operation, in particular resource-constrained environments. In this paper, an authenticated key establishment (AKE) and secure data sharing protocol is presented for grid system security without using public key settings. The proposed protocol extends delegation of resource server and user capability to proxy agents, and revocation of the delegated power. The proposed protocol shows its security strengths and efficiency in comparisons to other related protocols. Keywords: grid security; authentication; key establishment; delegation; GSI; grid security infrastructure.
Rule based anonymisation against inference attack in social networks by Nidhi Desai, Manik Lal Das Abstract: Social networks information has evolved as a powerful decision-maker in various facets of society. Meticulous scrutinisation of gigantic volumes of social data provides insightful solutions for forecasting, government policies, societal problems, business and strategic goals. The presence of sensitive information makes social network's information vulnerable to privacy concerns, leading to users' resistance to sharing their information confidently. Recently, inference attack using rule-based mining technique has posed a challenging privacy concern, particularly in social networks. This paper presents rule anonymity, a privacy model against inference attack using rule-based mining techniques in social networks. The proposed model considers adversary with strong knowledge of rule generation. The proposed rule-based anonymisation technique incorporates Rule Anonymity and ensures a strong privacy guarantee against an adversary with rule mining capability. The experimental results of the proposed anonymisation technique manifest the idea of rule anonymity on the social dataset. Keywords: social networks; data privacy; rule mining; inference attack; anonymity; privacy-preserving; social networks data publishing; adversarial model.
iCOPS: insider attack detection in distributed file systems by Riddhi Solani, Manik Lal Das Abstract: Distributed file system (DFS) has been widely used in many applications. Insider attacks in DFS is a potential target that can cause problems in many applications. A malicious insider or an outsider who controls an insider could compromise application's security by exploiting the target file(s) in the system. In this paper, a scheme, named as iCOPS, is proposed to detect insider attacks in DFSs. The proposed iCOPS scheme consists of two algorithms - Process Profiling and Attack Detection. The Process Profiling runs on datanode and replica nodes that provide output to namenode, whereas, the Attack Detection runs on the namenode to detect an attack that might have triggered by the Process Profiling algorithm. The analysis and experimental results of the proposed iCOPS show notable observations in detection of data alteration by insider attacks. Keywords: insider attacks; system security; distributed systems; HDFS; data modification; process profiling.
Distributed blockchains for collaborative product designs in smart cities by Shajulin Benedict Abstract: Personal fabrication tools such as 3D printers encourage designers for rapidly creating innovative prototypes of products. However, an immediate end product realisation is a challenge owing to several reasons, including permission delays. This paper proposes a blockchain-enabled architecture which explores the utilisation of decentralised connected 3D printers and decision-makers for collaboratively fabricating and installing societal products in smart cities - a social computing approach. Experiments were accomplished to manifest the proposed approach; besides, a case study of printing a TajMahal miniature was illustrated. The proposed mechanism would reap in lots of innovations and economic improvements in smart cities in the near future. Keywords: blockchain; collaborative design; fabrication; distributed ledger; smart city; social good applications.
Special Issue on: Ic-ETITE'20 Challenges in Cyber-Physical Systems Security
Detecting malicious users in the social networks using machine learning approach by H.L. Gururaj, U. Tanuja, V. Janhavi, B. Ramesh Abstract: Social networking plays a very important role in today's life. It helps to share ideas, information, multimedia messages and also provides the means of communication between the users. The popular social medias such as Facebook, Twitter, Instagram, etc., where the billions of data are being created in huge volume. Every user has their right to use any social media and a large number of users allowed malicious users by providing private or sensitive information, which results in security threats. In this research, they are proposing an natural language processing (NLP) technique to find suspicious users based on the daily conversations between the users. They demonstrated the behaviour of each user through their anomaly activities. Another machine learning technique called support vector machine (SVM) classifiers to detect the toxic comments in the comments blog. In this paper, the preliminary work concentrates on detecting the malicious user through the anomaly activities, behaviour profiles, messages and comment section. Keywords: social networks; malicious users; Naïve bayes; NLP; natural language processing; comments; social media; SVM; support vector machine.